Summary of Cjeval: a Benchmark For Assessing Large Language Models Using Chinese Junior High School Exam Data, by Qian-wen Zhang et al.
CJEval: A Benchmark for Assessing Large Language Models Using Chinese Junior High School Exam Data
by Qian-Wen Zhang, Haochen Wang, Fang Li, Siyu An, Lingfeng Qiao, Liangcai Gao, Di Yin, Xing Sun
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents CJEval, a benchmark for evaluating Large Language Models (LLMs) in real-world educational scenarios. The benchmark consists of 26,136 samples across four application-level tasks covering ten subjects, including questions, answers, and detailed annotations. This comprehensive dataset aims to bridge the gap between current academic benchmarks and industry requirements. The authors assess LLMs’ potential applications by fine-tuning on various educational tasks and conduct a thorough analysis of their performance. The study highlights opportunities and challenges in applying LLMs in education, emphasizing the need for more practical evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well Large Language Models (LLMs) can be used in real-life educational situations. Right now, there’s a big gap between what we know about LLMs from testing and what they’re actually capable of doing in the classroom. To help fill this gap, the authors created CJEval, a dataset with 26,136 examples across four different types of educational tasks. This dataset includes not only questions and answers but also extra information like question types, difficulty levels, and answer explanations. By using this benchmark, the authors showed that LLMs can be fine-tuned to perform well on specific educational tasks. The study highlights both the benefits and challenges of using LLMs in education. |
Keywords
» Artificial intelligence » Fine tuning
